We envision a future where large language models transcend their limitations as mere probabilistic chat tools to become dependable computational instruments seamlessly integrated into the fabric of enterprise applications. At dottxt, our mission is to transform how AI-generated data is produced, ensuring precision, reliability, and structured outputs that drive meaningful automation and insight.
Harnessing both cutting-edge proprietary technologies and a thriving open-source ecosystem, we build platforms that empower organizations to unlock the full potential of LLMs with 100% valid JSON outputs. Our approach eliminates uncertainty, enabling developers and enterprises to confidently embed AI into complex workflows requiring exacting data integrity.
As we scale and innovate, we are committed to shaping a new paradigm of AI adoption—one that fuses rigorous engineering with foundational openness to create tools that accelerate progress across industries and redefine what intelligent software can achieve.
Our Review
When we first encountered .txt (operating as dottxt), we'll admit we were skeptical. Another AI startup promising to "fix" large language models? But after digging into what founders Rémi Louf, Dan Gerlanc, and Brandon Willard have built, we're genuinely impressed by their approach to a real problem that's been driving developers crazy.
The team's origin story resonates with anyone who's wrestled with LLMs in production. They were at Normal Computing, trying to extract structured data from GPT-4, and kept hitting the same wall—unreliable outputs that required constant babysitting. Instead of accepting this as "just how AI works," they decided to engineer their way out of the problem.
The Technical Breakthrough That Actually Works
What caught our attention isn't just the promise of "100% valid JSON outputs"—it's that they've actually delivered on it. Their open-source Outlines library has been downloaded over 3 million times, and here's the kicker: OpenAI and Cohere are using their technology. When the giants in the space adopt your solution, you're probably onto something significant.
The core insight is elegant in its simplicity. Instead of hoping LLMs will produce structured data correctly, dottxt ensures they can't produce anything else. It's like putting guardrails on a highway—the model can still drive wherever it wants, but it can't veer off into invalid territory.
Why Enterprise Teams Are Paying Attention
We've seen plenty of AI tools that work great in demos but fall apart in real-world applications. What's different here is that dottxt is solving the reliability gap that keeps enterprises from fully embracing LLMs. When you're processing thousands of CVs or generating compliance reports, "mostly accurate" isn't good enough—you need guarantees.
The fact that they've raised $11.9 million from serious VCs like EQT Ventures and Elaia in just over a year tells us the market validation is real. These aren't vanity metrics—enterprise customers are willing to pay for structured, dependable AI outputs.
The Open-Source Strategy That's Actually Smart
Here's what we find refreshing: dottxt isn't doing the typical "open-source for marketing" play. They've built genuine value in their Outlines library while reserving their optimized platform and enterprise features for paying customers. It's a thoughtful balance that builds developer trust without giving away the farm.
For teams already struggling with unreliable LLM outputs in production, dottxt represents a practical path forward. They're not trying to reinvent AI—they're making existing AI actually usable for serious applications. Sometimes the best innovation is just making things work reliably.
Features
Ensure LLMs generate 100% valid JSON outputs
Produce structured data conforming to user-defined templates
Open-source Outlines library for structured data generation
Proprietary platform and API for reliable LLM output integration
Reduce manual workflow tasks like information extraction, classification, and synthetic data generation
Jobs
FAQs
Ploy was founded in 2024.
We provide a fast, reliable model that generates valid JSON 100% of the time via an API. For information extraction, classification, agents workflows, synthetic data generation.
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